Deep-Learning-Tutorial
It covers Assignments done in Deep Learning Course in Indraprastha Institute of Information Technology, Delhi (IIITD) and Udacity NanoDegree Intro to Deep Learning uisng Pytorch.
Table Of Contents of Deep Learning Assignments
| S.NO | TOPICS | PROJECT NAME |
|---|---|---|
| 01. | PTA for AND,OR,NOT and XOR and Madeline Implementation from scratch | Implementing PTA and Madeline |
| 02. | From scratch implementation of Back Propagation with optimizers Momentum, NAG, AdaGrad, RMSProp, Adam and initializations He, Xavier and Regularization using L1, L2 and Dropout. No Deep Learning library used. | Backpropagation Optimizers Regularization from scratch |
| 03. | CNN Implementation | Convolutional Neural Networks |
| 04. | Implemented Papers Show, Attend and Tell: Neural Image Caption Generation with Visual Attention and Interactive Attention Networks for Aspect-Level Sentiment Classification | Attention models |
Table Of Contents of Udacity Nanodegree course
| S.NO | TOPICS | PROJECT NAME |
|---|---|---|
| 01. | Implementing Gradient Descent over a set of random data | 1_GradientDescent |
| 02. | Simple Neural Network and common functions like tensor.view() tensor.reshape() tensor.shape tensor.rand | 2_Simple Neural Network and Random Functions |
| 03. | Creating Multi-Layer Neural Network and converting numpy array to tensors | 3_Multi Layer Neural Networks & numpy to torch |
| 04. | Digit Classification dataset using softmax and matrix multiplication(NO TRAINING) | 4_Digit Classification with Softmax (NO TRAINING) |
| 05. | pytorch nn module for complex neural networks , using torch.nn.functional | 5_Building networks with Pytorch - nn Module |
| 06. | other Activations , Neural Network using Relu and nn.Sequential , Changing weights and biases , using OrderedDict to name individual layers | 6_Relu Activation neural network and nn.Sequential |
| 07. | Training network over Digit Classification - loss calculation-criterion , Autograd , update weights using Pytorch -optim | 7_Training Neural Network |
| 08. | Training neural network to classify Fashion-MNIST | 8_Classifying Fashion-MNIST |
| 09. | Test over Test data , overfitting, regularization using Dropout and Accuracy Calculation | 9_Fashion MNIST - INFERENCE AND VALIDATION |
| 10. | Saving models using state_dict and training later on | _10_Saving and Loading Models |
| 11. | Making filters and visualising CNN | convulution-Neural-Network |
| 12. | Transfer learning | 12_CATS_VS_DOG_CLASSIFICATION_TRANSFER_LEARNING |
| 13. | STYLE TRANSFER | Style_Transfer |